MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution

• URL: http://arxiv.org/abs/2602.07529v2
• Date: Tue, 10 Feb 2026 03:03:52 GMT
• Title: MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution
• Authors: Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao,
• Abstract summary: We propose MedVerse, a reasoning framework for complex medical inference.<n>For data creation, we introduce the MedVerse Curator, which synthesizes knowledge-grounded medical reasoning paths.<n>We develop a customized inference engine that supports parallel execution without additional overhead.
• Score: 63.128360383691295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequential autoregressive decoding forces inherently parallel clinical reasoning, such as differential diagnosis, into a single linear reasoning path, limiting both efficiency and reliability for complex medical problems. To address this, we propose MedVerse, a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph (DAG) process based on Petri net theory. The framework adopts a full-stack design across data, model architecture, and system execution. For data creation, we introduce the MedVerse Curator, an automated pipeline that synthesizes knowledge-grounded medical reasoning paths and transforms them into Petri net-structured representations. At the architectural level, we propose a topology-aware attention mechanism with adaptive position indices that supports parallel reasoning while preserving logical consistency. Systematically, we develop a customized inference engine that supports parallel execution without additional overhead. Empirical evaluations show that MedVerse improves strong general-purpose LLMs by up to 8.9%. Compared to specialized medical LLMs, MedVerse achieves comparable performance while delivering a 1.3x reduction in inference latency and a 1.7x increase in generation throughput, enabled by its parallel decoding capability. Code is available at https://github.com/aiming-lab/MedVerse.

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